Explanations of Data Systems Concepts in CS Education: An Updated View on Notional Machines

As data-driven fields such as machine learning, visualization, and data science continue to grow, a solid foundation in data systems has become increasingly important. However, research on how students learn concepts of data systems remains limited, particularly regarding the role of notional machines. In this working group report, we examined educational materials, such as textbooks, to collect and categorize notional machines used across database subtopics. Our analysis shows variation in how notional machines are employed: most are presented visually, many rely on prior CS or database knowledge, and a significant number are under-specified, posing risks for student misconceptions. By highlighting well-defined examples and common patterns, this report provides a pedagogical resource for educators, supports the development of clearer instructional materials, and lays the groundwork for theory-building in data systems education.

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Published
2025-06-27
DOI
https://doi.org/10.1145/3760545.3783971
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
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article

Explanations of Data Systems Concepts in CS Education: An Updated View on Notional Machines

Laura Farinetti, Georgiana Haldeman, Giovanna Guerrini, Y.C. Pan et al.
Teaching and Learning Programming
article

Explanations of Data Systems Concepts in CS Education: An Updated View on Notional Machines

Laura Farinetti, Georgiana Haldeman, Giovanna Guerrini, Y.C. Pan, Martin Goodfellow, Daphne Miedema, Raja Sooriamurthi, Leonard Busuttil, Sujeeth Goud Ramagoni, Chandrika Satyavolu, Xiaoying Tu
article en

Abstract

As data-driven fields such as machine learning, visualization, and data science continue to grow, a solid foundation in data systems has become increasingly important. However, research on how students learn concepts of data systems remains limited, particularly regarding the role of notional machines. In this working group report, we examined educational materials, such as textbooks, to collect and categorize notional machines used across database subtopics. Our analysis shows variation in how notional machines are employed: most are presented visually, many rely on prior CS or database knowledge, and a significant number are under-specified, posing risks for student misconceptions. By highlighting well-defined examples and common patterns, this report provides a pedagogical resource for educators, supports the development of clearer instructional materials, and lays the groundwork for theory-building in data systems education.

Marquette University (US), North Carolina State University (US), Politecnico di Torino (IT), University of Strathclyde (GB), University of Toronto (CA), University of Malta (MT), Colgate University (US), Amsterdam University of the Arts (NL), Carnegie Mellon University (US), University of Genoa (IT), University of Amsterdam (NL)
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Openalex Percentile: Top 20%
Teaching and Learning Programming
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